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this is a base environment that allows for testing multiple path planning algorithms with different goal locations, and filtering algorithms

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MECH7710 Path Planning

Project for MECH7710 - Optimal Estimation implementing trajectory replanning methods for controlling a vehicle towards a goal in the presence of other vehicles. Vehicle Parameters are from a 2003 Infiniti G35.

Methods to Implement

Vehicle Models

  • 2 DOF Bicycle Model

Simulation Setup

Features

  • Class oriented approach, everything is modular
  • Each vehicle is comprised of 3 things:
    1. motion model class (currently a bicycle model)
    2. Filtering class (basic kalman filter estimating remote vehicle's orientation and yaw rate)
    3. Navigation class (currently uses Follow-the-gap (FTG) or A*)
  • Simulation can contain N number of vehicles, each with different components and goal destinations

How to run

  • demo.m runs the whole simulation
  • buildWorld() is where the vehicles and goal locations are created
  • FilterClass stores all process and sensor noise values, as well as a generic vehicle model

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this is a base environment that allows for testing multiple path planning algorithms with different goal locations, and filtering algorithms

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